The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
It is often useful for individuals (hereinafter “users”) viewing online articles of content, such as news articles, blog entries, and emails, to obtain further information about various subjects of the article, including people, places, organizations, topics, products, and so on (hereinafter referred to as “entities”). Copious searchable resources are available to provide this information, but for a variety of reasons, users often do not attempt to access the information available in these resources. For example, a user may find the process of explicitly searching for entities within an article tedious. Or, the user may be unaware that a search for a particular entity within the article would produce information of interest to the user. Or, the user may be unaware that a search for a related entity within the article would produce information of interest to the user. Or, the user may be unaware of the existence of various searchable resources.
One approach to overcoming these and other problems is for the content provider to manually search for interesting information about the entities within the article and include that information with the article. Unfortunately, this approach is labor intensive and relies upon the content provider becoming knowledgeable about the types of information available for each entity within the article.
Another approach is to pre-parse content before sending it to a user, and highlight entities of potential interest. The entities are located using a dictionary of interesting terms. The entities may be highlighted by, for instance, textual markups indicating a hyperlink. Upon clicking or hovering over the hyperlink, the user is presented with information about the highlighted entity, such as editorial information or search results.
Current approaches for identifying entities of interest are limited in that they require an editor to manually add entities of interest to a dictionary. It is difficult for an editor to anticipate, at general level, which entities within a specific article may actually be of interest within the context of that article. Moreover, as the context within which an article is viewed constantly changes, it becomes even more difficult to make a dictionary-based prediction of which entities will be of interest to a user. Furthermore, existing techniques still require a user to take potentially inconvenient steps to obtain information about an entity (e.g. clicking on a link and waiting for a new web page to load). The user may not be interested in taking such steps because of a lack of certainty as to the quality of the information that may be obtained about the entity. Furthermore, many existing approaches do not take into consideration the possibility that the user may also be interested in information about related entities that do not appear within an article.